Multiple erasure codes for distributed storage

ABSTRACT

Embodiments relate to dynamically selecting an erasure code. State data is tracked to ascertain frequency of file access. One of at least two erasure codes are selected based on the tracked state data in order to lower data recovery cost. The erasure code may be selected as either a product code or a local reconstruction code. Each erasure code includes a mode that is either a fast code or a compact code. The fast code features a low recovery cost and the compact code features a low storage overhead for less frequently accessed data. Data is encoded with one of the selected erasure codes and one of the modes of the selected erasure code. Data blocks are dynamically converted between the fast and compact codes of the selected erasure code responsive to a workload change.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application is a continuation patent application claiming thebenefit of the filing date of U.S. patent application Ser. No.14/600,532 filed on Jan. 20, 2015 and titled “Multiple Erasure Codes forDistributed Storage,” now pending, and which is hereby incorporated byreference.

BACKGROUND

The present embodiments relate to a new erasure coded storage systemthat adapts to workload changes by using two different erasure codes,including a fast code to optimize recovery cost of degraded reads andreconstruction of failed disks or nodes, and a compact code to providelow and bounded storage overhead. More specifically, the embodimentsrelates to a conversion mechanism to efficiently upcode and downcodedata blocks between the two codes.

Distributed storage systems storing multiple petabytes of data arebecoming common. These systems have to tolerate different failuresarising from unreliable components, software glitches, machine reboots,and maintenance operations. To guarantee high reliability andavailability despite these failures, data is replicated across multiplemachines. For example, it is known in some systems to maintain threecopies of each data block. Although disk storage is relativelyinexpensive, replication of the entire data footprint is infeasible atmassive scales of operation.

Many large scale distributed storage systems are transitioning to theuse of erasure codes, which are known to provide high reliability atlower storage cost. These systems use a single erasure code, whicheither optimizes for recovery cost or storage overhead. However, for anerasure coded system, reconstructing an unavailable block requiresfetching multiple data and parity blocks within the code stripe, whichresults in an increase in disk and network traffic. The increase in theamount of data to be read and transferred during recovery for anerasure-coded system results in high degraded read latency and longerreconstruction time.

SUMMARY

The embodiments include a system, computer program product, and methodfor dynamically selecting an erasure code in a storage system, andresponsively converting data blocks between different erasure codes.

A system, computer program product, and method are provided fordynamically selecting an erasure code. State data is tracked toascertain frequency of file access. One of at least two erasure codesare selected based on the tracked state data in order to lower datarecovery cost. The erasure code may either a product code or a localreconstruction code, with each erasure code having an associated modethat is either a fast code or a compact code. The fast code features alow recovery cost and the compact code features a low storage overheadfor less frequently accessed data. Data is encoded with one of theselected erasure codes and one of the modes of the selected erasurecode. Data blocks are dynamically converted between the fast and compactcodes of the selected erasure code responsive to a workload change.

Other features and advantages will become apparent from the followingdetailed description of the presently preferred embodiment(s), taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings referenced herein form a part of the specification.Features shown in the drawings are meant as illustrative of only someembodiments and not of all embodiments unless otherwise explicitlyindicated.

FIG. 1 depicts a block diagram illustrating components of an adaptivelycoded distributed file system architecture implemented as extensions toa distributed file system.

FIG. 2 depicts a flow chart illustrating a first extension of theadaptively coded distributed file system.

FIG. 3 depicts a state diagram illustrating two erasure codes for writecold files.

FIG. 4 depicts a flow chart illustrating a process for adapting to theworkload and dynamically converting files between fast and compactcodes.

FIG. 5 depicts a state diagram illustrating an adaptive coding modulefor a file that has been three-way replicated.

FIG. 6 depicts a block diagram illustrating downcoding product codesfrom compact to fast, also referred to herein as from PC_(comp) toPC_(fast).

FIG. 7 depicts a block diagram illustrating upcoding from PC_(fast) toPC_(comp).

FIG. 8 depicts a block diagram illustrating construction and codinginterfaces of the erasure coding module using local reconstruction codes(LRC).

FIG. 9 depicts a block diagram illustrating downcoding from LRC_(comp)to LRC_(fast).

FIG. 10 depicts an example of a cloud computing node.

FIG. 11 depicts a cloud computing environment.

FIG. 12 depicts a set of functional abstraction layers provided by thecloud computing environment.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentembodiment(s), as generally described and illustrated in the Figuresherein, may be arranged and designed in a wide variety of differentconfigurations. Thus, the following detailed description of theembodiments of the apparatus, system, and method, as presented in theFigures, is not intended to limit the scope, as claimed, but is merelyrepresentative of selected embodiments.

Reference throughout this specification to “a select embodiment,” “oneembodiment,” or “an embodiment” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment. Thus, appearances of the phrases“a select embodiment,” “in one embodiment,” or “in an embodiment” invarious places throughout this specification are not necessarilyreferring to the same embodiment.

The illustrated embodiments will be best understood by reference to thedrawings, wherein like parts are designated by like numerals throughout.The following description is intended only by way of example, and simplyillustrates certain selected embodiments of devices, systems, andprocesses that are consistent with the embodiments as claimed herein.

Erasure coding is a method of data protection in which data is brokeninto fragments, expanded and encoded with redundant pieces, andtypically stored across a set of different locations, such as disks,storage nodes or geographic locations. Embodiments disclosed hereinpertain to an erasure-coded storage system which uses two differenterasure codes from the same code family. The erasures codes describedherein are referred to as a fast code and a compact code. The fast codehas a low recovery cost for a small fraction of frequently accesseddata, and the compact code has low storage overhead for a majority ofless frequently accessed data.

At the same time, embodiments disclosed herein pertain to operations forconverting data between the two erasure codes. After initial encoding,the aspect of converting the data dynamically adapts to workload changesby using two operations to convert data blocks between the fast code andthe compact code. The two operations are referred to as upcoding anddowncoding. Upcoding refers to converting data blocks initially encodedwith fast code into a compact code and enables the system to reducestorage overhead. Downcoding refers to converting data blocks initiallyencoded with the compact code to a fast code representation to lowerassociated recovery cost. In one embodiment, operations associated withupcoding and downcoding only update the associated parity blocks whileconverting data blocks between the two codes. The coding of the datablocks is dynamic in that a conversion mechanism adaptively responds tosystem states, and more specifically changes therein. The coding andupcodes or downcodes associated data blocks responsive to the changes.

Referring to FIG. 1, a block diagram (100) is provided illustratingcomponents of an adaptively coded distributed file system architectureimplemented as extensions to a distributed file system. The adaptivesystem is shown as an extension to a RAID node (120) within adistributed file system (130). The node (120) is provided with aprocessor (122), also referred to herein as a processing unit, incommunication with memory (126) across a bus (124). The node (120) isfurther provided in communication with other nodes (135), which are eachin communication with persistent storage (128). Node (120) isresponsible for the storage and maintenance of data in persistentstorage (128). More specifically, node (120) is provided with one ormore tools to support dynamic selection of an erasure code for data in astorage system. As shown herein, and described in detail below, thetools embody an adaptive system comprised of three components, includingsystem states module (150), an adaptive coding module (160) and anerasure coding module (170). The adaptive coding module (160) maintainsthe system states module (150) of erasure coded data and manages statetransitions for ingested and stored data. The adaptive coding module(160) also interfaces (162) with the erasure coding module (170), whichfunctions to implement the different coding schemes.

The system states module (150) track the following file state associatedwith each erasure-coded data file: file size, last modification time,read count, and coding state. The file size and last modification timeare attributes maintained by the distributed file system, and used bythe adaptively coded distributed file system to compute the total datastorage and write age of the file. The adaptive coding module (160) alsotracks the read count of a file, which is the total number of readaccesses of the file by the distributed file system clients. The filestate can be updated on a read or write operation issued to the filefrom a distributed file system client.

The adaptive coding module (160) also maintains a global state, which isthe total storage used for data and parity. Every block in an erasurecoded data file has exactly one copy. Each erasure coded file is splitinto different erasure code stripes, with blocks in each stripedistributed across different nodes in the distributed file systemcluster. Each erasure coded data file has an associated parity filewhose size is determined by the coding scheme. The global state of thesystem is updated periodically when the adaptive coding module (160)initiates state transitions for erasure coded files. A state transitioncorresponds to a change in the coding state of a file and is invoked byusing interfaces to the erasure coding module.

The erasure coding module (170) exports four interfaces for coding data,including encode, decode, upcode and downcode. The encode operationrequires a data file and coding scheme as input, and generates a parityfile for all blocks in the data file. The decode operation is invoked ona degraded read for a block failure or as part of the reconstruction jobfor a disk or node failure. It also requires the index of a missing orcorrupted block in a file, and reconstructs the associated lost blockfrom the remaining data and parity blocks in the stripe using the inputcoding scheme. The following table illustrates the erasure codinginterfaces:

TABLE 1 Function Input Output Encode Data file; codec Parity file DecodeData file; parity file, codec, Recovered block lost block index UpcodeParity file, original fast Parity file encoded with codec, new compactcodec compact code Downcode Data file, parity file, Parity file encodedwith fast original compact code, new code fast codec

The adaptive coding module (160) invokes upcode and downcode operationsto adapt with workload changes and convert a data file representationbetween the two coding schemes. In one embodiment, both of theseconversion operations only update the associated parity file whenchanging the coding scheme of a data file. The upcode operationtransforms data from a fast code to a compact code representation, thusreducing the size of the parity file to achieve lower storage overhead.The upcode operation does not require reading the data file and is aparity-only transformation. The downcode operation transforms from acompact code to a fast code representation, thus reducing the recoverycost. The downcode operation requires reading both data and parityfiles, but only changes the parity file.

Referring to FIG. 2, a flow chart (200) is provided illustrating a firstextension of the adaptively coded distributed file system. A recentlycreated file is classified as a write hot file based on its lastmodified write time (202). The write hot file is three way replicated(204). At such time as the write accesses decrease below a set number ofaccesses, the write hot file is reclassified to a write cold file (206).In one embodiment, the file system is periodically scanned to selectwrite cold files for erasure coding. Each write cold file is alsoclassified based on the quantity of read access of data blocks in afile. More specifically, a read count for the file is ascertained (208),and as demonstrated herein, it is determined if the read count on thewrite cold file exceeds a threshold (210). In one embodiment, theassessment may be inverted to determine if the read count is below athreshold. A positive response to the determination at step (210) isfollowed by classifying the write cold file as a read hot file (212),and the file is encoded with a fast code (214). In one embodiment, thefast code has a low recovery cost. A negative response to thedetermination at step (212) is followed by classifying the write coldfile as a read cold file (216), indicating the file has a low readcount. Thereafter, the file is encoded with a compact code (218), whichin one embodiment has a low storage overhead. Accordingly, each writecold file is encoded based on an associated read count, with the encodedreflecting the frequency of read accesses to the file.

Referring to FIG. 3, a state diagram (300) is provided illustrating twoerasure codes for write cold files. As shown, a recently created file isclassified as write hot and is three way replicated (310). At such timeas modifications to the file decreases, the file is reclassified to awrite cold file (312). Each write cold file is encoded with an erasurecode based on the read accesses, also referred to herein as a readcount. Namely, a read cold file (320) is encoded with a compact code(322), and a read hot file (330) is encoded with a fast code (332).

As shown in FIG. 2 and FIG. 3, two different erasure codes are employedto initially encode a write cold file, using a fast code with lowrecovery cost for a small fraction of frequently accessed data, and acompact code with low storage overhead for a majority of less frequentlyaccessed data. However, a read cold file can transition into a read hotfile through later accesses, thereby requiring a lower recovery cost,and a read hot file can transition into a read cold file through adecrease in accesses, thereby requiring enabling a higher recovery cost.

Referring to FIG. 4, a flow chart (400) is provided illustrating aprocess for adapting to the workload and dynamically converting filesbetween fast and compact codes. The conversion for a file is guided byits own file state, e.g. read count, as well as the global system state,e.g. total storage. It is initially determined if the storage consumedby data and parity blocks exceed a boundary (402). A positive responseto the determination at step (402) initiates upcoding of one of morefast coded files to reduce storage space. The files that have compactcodes are sorted based on their read counts (404). One or more fileswith the lowest read counts are upcoded from the fast erasure code tothe compact erasure code (406). Following step (406), the processrepeats the assessment at step (402), namely to assess for storageconsumption with respect to the boundary (408). More specifically, therepeated assessment at step (408) ascertains if additional storage spaceis required in view of the upcoding at step (406). If there is a needfor additional storage space, one or more replicated files are selectedand encoded directly into the compact erasure code, and if necessary,one or more replicated or fast coded files are upcoded to the compacterasure code (410). In one embodiment, the file(s) selected at step(410) are based on their read count(s), with the file with the lowestread counts selected for upcoding to the compact erasure code to makethe total storage overhead bounded again. If at step (402) it isdetermined that the storage consumed does not exceed the boundary of thestorage overhead or if at step (408) it is determined that the storageconsumed does not exceed the boundary, one or more files may be selectedand transitioned from compact encoding to the fast erasure code (412).In one embodiment, the file(s) selected at step (412) may be based on anassociated read count, and specifically on when the last read accesstook place. The conversion of the files to the fast erasure code reducesthe recovery cost of a future degraded read to a file, which was earliercompact coded, but in one embodiment has recently been accessed.

Referring to FIG. 5, a state diagram (500) is provided illustrating anadaptive coding module for a recently created file (505) that has beenthree way replicated (510). Similar to FIG. 3, at such time asmodifications to the file decreases, the file is reclassified to a writecold file (512). Each write cold file is encoded with an erasure codebased on the read accesses, also referred to herein as a read count. Atthe same time, the encoding also addresses the storage capacityboundary. Namely, a read cold file (520) is encoded with a compact code(522) in view of the state of the storage system capacity, and a readhot file (530) is encoded with a fast code (532) also in view of thestate of the storage system capacity. The state diagram shown herein isadaptive, and responds to the capacity of the storage system. At suchtime as the storage capacity has reached or exceeds its threshold, e.g.boundary, modifications to one or more files take place, as shown anddescribed in FIG. 4. More specifically, one or more read cold files thatwere fast encoded are compact encoded (540). Similarly, at such time asthe storage capacity is within its boundary, one or more read cold filesthat were compact encoded files may be selected and fast encoded (550).The state diagram functions as a temperature measurement with respect tostorage capacity, and dynamically converts select files between the twoerasure codes to balance recovery cost with storage overhead. Theadaptive coding module tracks the system states and invokes thedifferent coding interfaces. Accordingly, the state diagram illustratesemploying the erasure codes to adapt with workload changes and systemstorage by converting between fast and compact codes.

Two new code families are employed as part of the adaptive file systemerasure coding module, including product codes and local reconstructioncodes. Product codes are two dimensional codes that provide low recoverycost. Referring to FIG. 6, a block diagram (600) is providedillustrating downcoding product codes from compact to fast, alsoreferred to herein as from PC_(comp) to PC_(fast). The downcodingoperation converts a single PC_(comp) code (610) into three PC_(fast)codes (630), (640), and (650). Only the horizontal and global parityblocks change between the PC_(comp) (610) and the three PC_(fast) codes(630), (640), and (650). As shown, computing the horizontal and globalparity blocks in the first two PC_(fast) codes (630) and (640) requiresnetwork transfers and exclusive OR (hereinafter XOR) operations (660)and (670) over the data blocks in the two horizontal rows of thePC_(comp) code, namely XOR (660) for rows (612) and (614) and XOR (670)for rows (616) and (618). The horizontal and global parity blocks in thethird PC_(fast) code (650) is computed through an XOR operation (680)from the horizontal and global parity blocks (620) and the newlycomputed XOR operations at (660) and (670) of the first two PC_(fast)codes (630) and (640). This optimization saves on the network transfersof two horizontal rows of data blocks. Data and vertical parity blocksin the resulting three PC_(fast) codes (630), (640), and (650) remainunchanged from the PC_(comp) code (610) and do not require any networktransfers. Accordingly, as shown herein downcoding from a compact codeto a fast code only requires vertical parities to be recomputed.

Referring to FIG. 7, a block diagram (700) is provided illustratingupcoding from PC_(fast) to PC_(comp). Upcoding is shown herein as anefficient parity only conversion operation for product codes. The upcodeprocess performs XOR (750) over the old horizontal and global parityblocks of three PC_(fast) (710), (720), (730) codes to compute the newhorizontal parity blocks (780), (782), (784), (786), and (788) and theglobal parity block (790). New vertical parity blocks (760), (762),(764), (766), (768), and (770) are computed from the old verticalparities (712) and (714), (722) and (724), and (732) and (734), e.g. thevertical parities remain the same. Accordingly, the upcoding operationdoes not require any network transfers of the data blocks from the threePC_(fast) codes to compute the new parities in the PC_(comp) code.

Referring to FIG. 8, a block diagram (800) is provided illustratingconstruction and coding interfaces of the erasure coding module usinglocal reconstruction codes (LRCs). More specifically, the exampleconstruction of the LRC_(fast) codes is shown with twelve data blocks(802)-(832), six local parity blocks (842)-(852), and two global parityblocks (860) and (862). The encode operation of the LRC code computesthe local parities by performing two XOR operations, each over a groupof parity blocks. A first XOR (870) is performed over the parity blocks(842), (844), and (846), and a second XOR (872) is performed over parityblocks (846), (848), and (850). Namely, two local parities (882) and(884) in the LRC_(comp) code are computed as an XOR over three localparities (842), (844), (846), and (848), (850), and (852) in theLRC_(fast) code. As a result, the adaptive system requires only sixnetwork transfers to compute the two new local parities if theLRC_(comp) code in an upcode operation.

The two global parity blocks (860) and (862) are computed by performinga Reed-Solomon encoding over all of the twelve data blocks (802)-(832).The LRC_(comp) representation is shown at (880) with the same twelvedata blocks (802)-(832), two local parity blocks (882) and (884), andtwo global parity blocks (886) and (888). As shown, a single localparity block (882) is the XOR of data blocks (802), (804), (806), (822),(824), and (826), and a single local parity block (884) is the XOR ofdata blocks (808), (810), (812), (828), (830), and (832). Any singlefailure in data or local parity blocks for LRC_(fast) requires two blocktransfer from the same column for reconstruction. However, a failure ina global parity block requires all twelve data blocks forreconstruction.

Referring to FIG. 9, a block diagram (900) is provided illustratingdowncoding from LRC_(comp) to LRC_(fast). Each of two local parityblocks (982) and (984) is associated with a group of six data blocks.Namely, local parity block (982) is associated with data blocks (902),(904), (906), (922), (924), and (926), and local parity block (984) isassociated with data blocks (908), (910), (912), (928), (930), and(932). Recovering a lost data block or local parity requires six blocktransfers from its group in the LRC_(comp) code. As a result, theLRC_(comp) code also has a lower recovery cost for degraded reads. Twoof the three new local parities in the LRC_(fast) code are computed at(970) and (972) from the data blocks in the individual columns of theLRC_(comp) code. The third local parity is computed (974) by performingan XOR over the two new local parities and the old local parity (982) inthe LRC_(comp) code. The downcode operation requires ten block transfersfor computing the new local parities. The global parities (986) and(988) remain unchanged and do not require any network transfers.

Upcode and downcode operations for the LRC codes are similar to that ofthe product codes. However, upcoding and downcoding with LRC codesrequires several data block transfers to compute the new local andglobal parities. As shown in the description of the product and localreconstruction codes, the upcoding operation either merges three fastcodes for product codes or collapses one fast code for LRC codes into anew compact code of smaller size. Downcoding performs the reversesequence of steps. Both operations change the coding state of the datafile and reduce its replication level to one.

The tools shown and described herein support dynamically selecting anerasure code in a storage system, and adaptively converting data betweenthe erasure codes based on system measurements. In one embodiment, thefunctionality and support of the erasure codes and the associatedconversion between the codes may be extrapolated to a cloud computingenvironment with a shared pool of resources.

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes. Referring now to FIG. 10, a schematicof an example of a cloud computing node is shown. Cloud computing node(1010) is only one example of a suitable cloud computing node and is notintended to suggest any limitation as to the scope of use orfunctionality of embodiments described herein. Regardless, cloudcomputing node (1010) is capable of being implemented and/or performingany of the functionality set forth hereinabove. In cloud computing node(1010) there is a computer system/server (1012), which is operationalwith numerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with computer system/server (1012) include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system/server (1012) may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server (1012) may be practiced in distributedcloud computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed cloud computing environment, program modules may belocated in both local and remote computer system storage media includingmemory storage devices.

As shown in FIG. 10, computer system/server (1012) in cloud computingnode (1010) is shown in the form of a general-purpose computing device.The components of computer system/server (1012) may include, but are notlimited to, one or more processors or processing units (1016), systemmemory (1028), and a bus (1018) that couples various system componentsincluding system memory (1028) to processor (1016). Bus (1018)represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include an Industry Standard Architecture (ISA) bus,a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus,Video Electronics Standards Association (VESA) local bus, and aPeripheral Component Interconnects (PCI) bus. A computer system/server(1012) typically includes a variety of computer system readable media.Such media may be any available media that is accessible by a computersystem/server (1012), and it includes both volatile and non-volatilemedia, and removable and non-removable media.

System memory (1028) can include computer system readable media in theform of volatile memory, such as random access memory (RAM) (1030)and/or cache memory (1032). Computer system/server (1012) may furtherinclude other removable/non-removable, volatile/non-volatile computersystem storage media. By way of example only, storage system (1034) canbe provided for reading from and writing to a non-removable,non-volatile magnetic media (not shown and typically called a “harddrive”). Although not shown, a magnetic disk drive for reading from andwriting to a removable, non-volatile magnetic disk (e.g., a “floppydisk”), and an optical disk drive for reading from or writing to aremovable, non-volatile optical disk such as a CD-ROM, DVD-ROM or otheroptical media can be provided. In such instances, each can be connectedto bus (1018) by one or more data media interfaces. As will be furtherdepicted and described below, memory (1028) may include at least oneprogram product having set (e.g., at least one) of program modules thatare configured to carry out the functions of the embodiment(s).

Program/utility (1040), having a set (at least one) of program modules(1042), may be stored in memory (1028) by way of example, and notlimitation, as well as an operating system, one or more applicationprograms, other program modules, and program data. Each of the operatingsystems, one or more application programs, other program modules, andprogram data or some combination thereof, may include an implementationof a networking environment. Program modules (1042) generally carry outthe functions and/or methodologies of embodiments as described herein.

Computer system/server (1012) may also communicate with one or moreexternal devices (1014), such as a keyboard, a pointing device, adisplay (1024), etc.; one or more devices that enable a user to interactwith computer system/server (1012); and/or any devices (e.g., networkcard, modem, etc.) that enables computer system/server (1012) tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces (1022). Still yet, computersystem/server (1012) can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter (1020). Asdepicted, network adapter (1020) communicates with the other componentsof computer system/server (1012) via bus (1018). It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system/server (1012). Examples,include, but are not limited to: microcode, device drivers, redundantprocessing units, external disk drive arrays, RAID systems, tape drives,and data archival storage systems, etc.

Referring now to FIG. 11, illustrative cloud computing environment(1150) is depicted. As shown, cloud computing environment (1150)comprises one or more cloud computing nodes (1110) with which localcomputing devices used by cloud consumers, such as, for example,personal digital assistant (PDA) or cellular telephone (1154A), desktopcomputer (1154B), laptop computer (1154C), and/or automobile computersystem (1154N) may communicate. Nodes (1110) may communicate with oneanother. They may be grouped (not shown) physically or virtually, in oneor more networks, such as Private, Community, Public, or Hybrid cloudsas described hereinabove, or a combination thereof. This allows cloudcomputing environment (1150) to offer infrastructure, platforms, and/orsoftware as services for which a cloud consumer does not need tomaintain resources on a local computing device. It is understood thatthe types of computing devices (1154A)-(1154N) shown in FIG. 11 areintended to be illustrative only and that computing nodes (1110) andcloud computing environment (1150) can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 12, a set of functional abstraction layersprovided by cloud computing environment (1200) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 12 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided: hardware and software layer (1210),virtualization layer (1220), management layer (1230), and workload layer(1240). The hardware and software layer (1210) includes hardware andsoftware components. Examples of hardware components include mainframes,in one example IBM® zSeries® systems; RISC (Reduced Instruction SetComputer) architecture based servers, in one example IBM pSeries®systems; IBM xSeries® systems; IBM BladeCenter® systems; storagedevices; networks and networking components. Examples of softwarecomponents include network application server software, in one exampleIBM WebSphere® application server software; and database software, inone example IBM DB2® database software. (IBM, zSeries, pSeries, xSeries,BladeCenter, WebSphere, and DB2 are trademarks of International BusinessMachines Corporation registered in many jurisdictions worldwide).

Virtualization layer (1220) provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, a management layer (1230) may provide the followingfunctions: resource provisioning, metering and pricing, user portal,service level management, and key management. The functions aredescribed below. Resource provisioning provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and pricingprovides cost tracking as resources that are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Key management provides cloudcomputing and sharing of data among two or more entities such thatrequired encryption and management of associated encrypted data are met.

Workloads layer (1240) provides examples of functionality for which thecloud computing environment may be utilized. In the shared pool ofconfigurable computer resources described herein, hereinafter referredto as a cloud computing environment, files may be shared among userswithin multiple data centers, also referred to herein as data sites.Accordingly, a series of mechanisms are provided within the shared poolto support organization and management of data storage within the cloudcomputing environment.

As will be appreciated by one skilled in the art, aspects of the presentembodiment(s) may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present embodiment(s) may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Furthermore, aspects of the present embodiment(s) may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent embodiment(s) may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present embodiment(s) are described above with referenceto flow chart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments. Itwill be understood that each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, can be implemented by computerprogram instructions.

These computer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flow charts and/orblock diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function(s)/act(s) specified in the flow chart and/orblock diagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions, which execute on thecomputer or other programmable apparatus, provide processes forimplementing the functions/acts specified in the flow chart and/or blockdiagram block or blocks.

The flow charts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments. In this regard, each block in the flow charts or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flow chart illustration(s), and combinations ofblocks in the block diagrams and/or flow chart illustration(s), can beimplemented by special purpose hardware-based systems that perform thespecified functions or acts, or combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

The present embodiment(s) may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects.

Computer readable program instructions described herein can bedownloaded respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmissions, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Aspects of the present embodiments are described herein with referenceto flow chart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments. Itwill be understood that each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, can be implemented by computerreadable program instructions.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present embodiment(s) has been presented for purposesof illustration and description, but is not intended to be exhaustive orlimited to the form disclosed. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope and spirit. The embodiment was chosen and described in orderto best explain the principles and the practical application, and toenable others of ordinary skill in the art to understand the variousembodiments with various modifications as are suited to the particularuse contemplated. Accordingly, the implementation of two or more erasurecodes and specifically the dynamic manner in which data is encoded andadaptively converted to a different code provides a new and efficientinterface for achieving improved recovery cost and storage efficiency.

It will be appreciated that, although specific embodiments have beendescribed herein for purposes of illustration, various modifications maybe made without departing from the spirit and scope. Accordingly, thescope of protection is limited only by the following claims and theirequivalents.

What is claimed is:
 1. A computer system comprising: a processor incommunication with data storage; a tool in communication with theprocessor to dynamically select an erasure code, including: a systemstates module to track state data to ascertain frequency of file access;an adaptive coding module to select one of at least two erasure codesbased on the tracked state data, the selection to lower data recoverycost, the at least two erasure codes selected from the group consistingof: a product code and a local reconstruction code, each erasure codeincluding a mode selected from the group consisting of: a fast code anda compact code, the fast code having a low recovery cost and the compactcode having a low storage overhead for less frequently accessed data; anencoding module to encode the data with one of the selected erasurecodes and one of the modes of the selected erasure code; and theadaptive coding module to dynamically convert data blocks between thefast and compact codes of the selected erasure code responsive to aworkload change.
 2. The system of claim 1, further comprising theadaptive coding module to dynamically convert between fast and compactlocal reconstruction code data blocks and dynamically convert betweenfast and compact product code data blocks, wherein the convertingchanges a coding state of an associated data file.
 3. The system ofclaim 2, further comprising the adaptive coding module to downcode datablocks from compact product code to fast product code, whereindowncoding is limiting to re-computing of vertical parities.
 4. Thesystem of claim 2, further comprising the adaptive coding module toupcode data blocks from fast product code to compact product code,wherein upcoding is limited to a parity only conversion operation. 5.The system of claim 2, further comprising the adaptive coding module todowncode data blocks from compact local reconstruction code to fastlocal reconstruction code, wherein downcoding is limited to computingnew local parities.
 6. The system of claim 2, further comprising theadaptive coding module to upcode data blocks from fast localreconstruction code to compact local reconstruction code, whereinupcoding includes computing new local and global parities.
 7. A computerprogram product for dynamically selecting an erasure code in a storagesystem, the computer program product comprising a computer readablestorage device having program code embodied therewith, the program codeexecutable by a processing unit to: track a state data to ascertainfrequency of file access; select one of at least two erasure codes basedon the tracked state data, the selection to lower data recovery cost,the at least two erasure codes selected from the group consisting of: aproduct code and a local reconstruction code, each erasure codeincluding a mode selected from the group consisting of: a fast code anda compact code, the fast code having a low recovery cost and the compactcode having a low storage overhead for less frequently accessed data;encode the data with one of the selected erasure codes and one of themodes of the selected erasure code; and dynamically convert data blocksbetween the fast and compact codes of the selected erasure coderesponsive to a workload change.
 8. The computer program product ofclaim 7, further comprising program code to dynamically convert betweenfast and compact local reconstruction code data blocks and dynamicallyconvert between fast and compact product code data blocks, wherein theconverting changes a coding state of an associated data file.
 9. Thecomputer program product of claim 8, further comprising program code todowncode data blocks from compact product code to fast product code,wherein downcoding is limiting to re-computing of vertical parities. 10.The computer program product of claim 8, further comprising program codeto upcode data blocks from fast product code to compact product code,wherein upcoding is limited to a parity only conversion operation. 11.The computer program product of claim 8, further comprising program codeto downcode data blocks from compact local reconstruction code to fastlocal reconstruction code, wherein downcoding is limited to computingnew local parities.
 12. The computer program product of claim 8, furthercomprising program code to upcode data blocks from fast localreconstruction code to compact local reconstruction code, whereinupcoding includes computing new local and global parities.
 13. A methodcomprising: tracking, by a system module, state data to ascertainfrequency of file access; selecting, by an erasure coding module, one ofat least two erasure codes based on the tracked state data, theselection to lower data recovery cost, the at least two erasure codesselected from the group consisting of: a product code and a localreconstruction code, each erasure code including a mode selected fromthe group consisting of: a fast code and a compact code, the fast codehaving a low recovery cost and the compact code having a low storageoverhead for less frequently accessed data; encoding, by the erasurecoding module, the data with one of the selected erasure codes and oneof the modes of the selected erasure code; and dynamically converting,by an adaptive coding module, data blocks between the fast and compactcodes of the selected erasure code responsive to a workload change. 14.The method of claim 13, further comprising dynamically converting, bythe adaptive coding module, between fast and compact localreconstruction code data blocks and dynamically converting between fastand compact product code data blocks, wherein the converting changes acoding state of an associated data file.
 15. The method of claim 14,further comprising downcoding data blocks, by the erasure coding module,from compact product code to fast product code, wherein the downcodingis limiting to re-computing of vertical parities.
 16. The method ofclaim 14, further comprising upcoding data blocks by the erasure codingmodule, from fast product code to compact product code, wherein theupcoding is limited to a parity only conversion operation.
 17. Themethod of claim 14, further comprising downcoding data blocks, by theadaptive coding module, from compact local reconstruction code to fastlocal reconstruction code, wherein the downcoding is limited tocomputing new local parities.
 18. The method of claim 14, furthercomprising upcoding data blocks by the adaptive coding module, from fastlocal reconstruction code to compact local reconstruction code, whereinthe upcoding includes computing new local and global parities.